Contribution of Three Forearm Muscles to Wrist and Finger Extension
Erik Sosso - Politecnico di Torino - [2006-07]
This work was developed within the collaboration between CREB (Centre de Recerca en Enginyeria Biomèdica) of Barcelona (Spain) and LISiN (Laboratorio di Ingegneria del Sistema Neuromuscolare e della riabilitazione motoria) of Turin (Italy), during Erasmus European ex-change project lasted 6 month.
Electromyographic (EMG) signal recorded over the skin is constituted by the superposi-tion of extra cellular potentials of the muscular fibers. The functional unit of the muscle is called motor unit, which includes all the fibers innervated by the same motor neuron. The su-perficial signal is composed by contributions of the motor units active within the muscle. However, when muscles are close to each others and simultaneously active, surface EMG sig-nals can be affected by the crosstalk phenomenon, defined as the EMG signal detected over a non-active muscle and generated by a nearby muscle. The forearm region represents an optimal zone to in-vestigate this phenomenon because the forearm muscles are very close to each others. The muscles considered in this work are the Extensor Carpi Radialis (ECR), Extensor Carpi Ul-naris (ECU) and Extensor Digitorum Communis (EDC): they allow the extension of the wrist in radial and ulnar deviation, and the medium and ring fingers extension. The first chapter in-troduces the electromyographic signal, with a special regard to his generation and the existing techniques used to record the signals. The chapter provides also a general overview of the anatomy and physiology of the forearm muscles.
The work was structured in three parts. In the first part, an innovative technique that al-lows the subjects and the operator to understand in real-time the activation level of each mus-cle during different kinds of exercise of the wrist is developed. This technique is based on the concept of biofeedback, which is defined as the technique that allows the subject to know in real-time the level of a specific physiological variable, in order to allow him to control such a variable. Biofeedback systems are largely used in clinical practice. The software developed in this work is called Forearm Biofeedback Software (FBS) and was developed at CREB in Bar-celona. National Instruments Labview© was used to implement FBS algorithm. The software was composed by three windows. The first window permits the subject training, the second allows the extraction of the Maximum Voluntary Contraction (MVC) (that is the maximum effort level that the subject can perform for a single exercise), and the third window allows the recording of the EMG signals during several submaximal extension of the wrist in three dif-ferent directions (the most selective direction for each muscle). More details about FBS are presented in Chapter 2.
The second part of this work is constituted by the experimental sessions. Seven healthy male subjects with age ranging from 19 to 27 years (average: 23.7 years, std. dev: 2.1 years) participated in this experiment after giving informed consent. Surface electromyographic sig-nals from the forearm extensors were collected in isometric conditions by acting on a me-chanical brace, developed at LISiN for working with FBS. Chapter 3 describes the experimen-tal sessions.
The third part of this work is constituted by the analysis of the data set collected during the experimental sessions, and it is described in Chapter 4. The study about the selectivity of the wrist exercises is explained. A new index, Muscular Selectivity Index (MSI), is introduced in order to investigate the muscular selectivity of the wrist exercise. MSI is based on the root mean squared (RMS) parameters of the EMG signals, and gives information about the real level of selectivity during an exercise. The index is also used to provide additional information about the exercises and the crosstalk terms. Reliability of EMG signal variables was assessed, using the Intraclass Correlation Coefficient (ICC). ICC is a commonly used index of reliability and indicates the percentage of global variance that can be attributed to the variability between subjects. The extent to which FBS can be of use with the measurements is also assessed.
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